class_docstr: A generic recurrent neural network decoder
  
  # Arguments
  rnns: rnn layers used for decoder, support stacked rnn layers
  embedding: embedding layer in decoder
  input_shape: shape of input, not including batch
  
  >>> decoder = RNNDecoder.initialize("lstm", 2, 3)
  creating: createZooKerasLSTM
  creating: createZooKerasLSTM
  creating: createZooKerasRNNDecoder
  
  >>> lstm = LSTM(3)
  creating: createZooKerasLSTM
  >>> embedding = Embedding(1000, 32, input_length=10, name="embedding1")
  creating: createZooKerasEmbedding
  >>> encoder = RNNDecoder([lstm], embedding)
  creating: createZooKerasRNNDecoder
docstr: rnn_type: currently support "simplernn | lstm | gru"
  nlayers: number of layers used in decoder
  hidden_size: hidden size of decoder
  embedding: embedding layer in decoder, `None` is supported
docstr_tok: ["rnn_type",":","currently","support","``","simplernn","|","lstm","|","gru","''","nlayers",":","number","of","layers","used","in","decoder","hidden_size",":","hidden","size","of","decoder","embedding",":","embedding","layer","in","decoder",",","`","None","`","is","supported"]
code: def initialize(
      cls,
      rnn_type,
      nlayers,
      hidden_size,
      embedding=None,
      input_shape=None,
  ):
      """
      rnn_type: currently support "simplernn | lstm | gru"
      nlayers: number of layers used in decoder
      hidden_size: hidden size of decoder
      embedding: embedding layer in decoder, `None` is supported
      """
      rnns = createRNN(rnn_type, nlayers, hidden_size)
      return RNNDecoder(rnns, embedding, input_shape)
  
imports: from bigdl.nn.layer import Layer
  from zoo.pipeline.api.keras.layers import *
  from zoo.models.common import ZooModel
  from zoo.common.utils import callZooFunc
  from zoo.pipeline.api.keras.engine import ZooKerasLayer
  from zoo.pipeline.api.keras.models import Model
label_desc: Use image node_id 2 for calling the RNNDecoder obj's underlying member method code with example usage: obj.initialize(cls, rnn_type, nlayers, hidden_size, embedding, input_shape) and returns: RNNDecoder